Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control

TL;DR

This paper introduces a distributed convex feasible set based model predictive control approach for multi-vehicle motion coordination, enabling real-time, collision-free trajectories in complex autonomous driving scenarios.

Contribution

It proposes a novel distributed MPC method using convex feasible sets to convexify collision constraints, improving real-time performance and deadlock resolution in multi-agent systems.

Findings

Real-time collision-free trajectory computation demonstrated.

Method outperforms centralized MPC and RVO in efficiency.

Robustness to tracking errors confirmed through multiple scenarios.

Abstract

The implementation of optimization-based motion coordination approaches in real world multi-agent systems remains challenging due to their high computational complexity and potential deadlocks. This paper presents a distributed model predictive control (MPC) approach based on convex feasible set (CFS) algorithm for multi-vehicle motion coordination in autonomous driving. By using CFS to convexify the collision avoidance constraints, collision-free trajectories can be computed in real time. We analyze the potential deadlocks and show that a deadlock can be resolved by changing vehicles' desired speeds. The MPC structure ensures that our algorithm is robust to low-level tracking errors. The proposed distributed method has been tested in multiple challenging multi-vehicle environments, including unstructured road, intersection, crossing, platoon formation, merging, and overtaking scenarios. The numerical results and comparison with other approaches (including a centralized MPC and reciprocal velocity obstacles) show that the proposed method is computationally efficient and robust, and avoids deadlocks.